General Info

This is the final project for the CS224N Course in Stanford (Natural Language Processing with Deep Learning). In this project, I extended the BiDAF architecture by substituting the Recurrent layers with transformer layers and by adding a character embedding module. A detailed report about my implementation and experimentation is included in the pdf report.

The base model is taken from: https://github.com/MasoudKaviani/Question-Answering-based-on-SQuAD

Setup

1. Make sure you have Miniconda installed

   1. Conda is a package manager that sandboxes your project’s dependencies in a virtual environment
   2. Miniconda contains Conda and its dependencies with no extra packages by default (as opposed to Anaconda, which installs some extra packages)

2. cd into src, run conda env create -f environment.yml

   1. This creates a Conda environment called squad

3. Run source activate squad

   1. This activates the squad environment
   2. Do this each time you want to write/test your code

4. Run python setup.py

   1. This downloads SQuAD 2.0 training and dev sets, as well as the GloVe 300-dimensional word vectors (840B)
   2. This also pre-processes the dataset for efficient data loading
   3. For a MacBook Pro on the Stanford network, setup.py takes around 30 minutes total

5. Browse the code in train.py

   1. The train.py script is the entry point for training a model. It reads command-line arguments, loads the SQuAD dataset, and trains a model.
   2. You may find it helpful to browse the arguments provided by the starter code. Either look directly at the parser.add_argument lines in the source code, or run python train.py -h.